Overview

Dataset statistics

Number of variables43
Number of observations23413
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 MiB
Average record size in memory178.0 B

Variable types

Numeric17
Categorical26

Alerts

Annual_Income is highly overall correlated with Monthly_Inhand_Salary and 2 other fieldsHigh correlation
Monthly_Inhand_Salary is highly overall correlated with Annual_Income and 2 other fieldsHigh correlation
Num_Bank_Accounts is highly overall correlated with Interest_Rate and 6 other fieldsHigh correlation
Num_Credit_Card is highly overall correlated with Interest_Rate and 2 other fieldsHigh correlation
Interest_Rate is highly overall correlated with Num_Bank_Accounts and 7 other fieldsHigh correlation
Num_of_Loan is highly overall correlated with Num_Bank_Accounts and 6 other fieldsHigh correlation
Delay_from_due_date is highly overall correlated with Num_Bank_Accounts and 10 other fieldsHigh correlation
Num_of_Delayed_Payment is highly overall correlated with Num_Bank_Accounts and 6 other fieldsHigh correlation
Num_Credit_Inquiries is highly overall correlated with Num_Bank_Accounts and 6 other fieldsHigh correlation
Outstanding_Debt is highly overall correlated with Num_Bank_Accounts and 11 other fieldsHigh correlation
Credit_History_Age is highly overall correlated with Num_Bank_Accounts and 9 other fieldsHigh correlation
Amount_invested_monthly is highly overall correlated with Annual_Income and 1 other fieldsHigh correlation
Monthly_Balance is highly overall correlated with Annual_Income and 1 other fieldsHigh correlation
Credit_Mix is highly overall correlated with Outstanding_Debt and 3 other fieldsHigh correlation
Credit_Score is highly overall correlated with Delay_from_due_date and 5 other fieldsHigh correlation
Payment_of_Min_Amount_No is highly overall correlated with Delay_from_due_date and 5 other fieldsHigh correlation
Payment_of_Min_Amount_Yes is highly overall correlated with Delay_from_due_date and 5 other fieldsHigh correlation
Occupation_Accountant is highly imbalanced (65.8%)Imbalance
Occupation_Architect is highly imbalanced (66.2%)Imbalance
Occupation_Developer is highly imbalanced (66.9%)Imbalance
Occupation_Doctor is highly imbalanced (67.5%)Imbalance
Occupation_Engineer is highly imbalanced (64.7%)Imbalance
Occupation_Entrepreneur is highly imbalanced (66.1%)Imbalance
Occupation_Journalist is highly imbalanced (66.8%)Imbalance
Occupation_Lawyer is highly imbalanced (66.2%)Imbalance
Occupation_Manager is highly imbalanced (66.5%)Imbalance
Occupation_Mechanic is highly imbalanced (66.6%)Imbalance
Occupation_Media_Manager is highly imbalanced (68.1%)Imbalance
Occupation_Musician is highly imbalanced (67.4%)Imbalance
Occupation_Teacher is highly imbalanced (64.9%)Imbalance
Occupation_Writer is highly imbalanced (69.2%)Imbalance
Payment_Behaviour_Low_spent_Large_value_payments is highly imbalanced (51.9%)Imbalance
Credit_Utilization_Ratio has unique valuesUnique
Num_Bank_Accounts has 1305 (5.6%) zerosZeros
Num_of_Loan has 2301 (9.8%) zerosZeros
Delay_from_due_date has 353 (1.5%) zerosZeros
Num_of_Delayed_Payment has 522 (2.2%) zerosZeros
Num_Credit_Inquiries has 1454 (6.2%) zerosZeros
Total_EMI_per_month has 2162 (9.2%) zerosZeros

Reproduction

Analysis started2023-06-03 15:54:18.634089
Analysis finished2023-06-03 15:55:00.691424
Duration42.06 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.168496
Minimum14
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size205.9 KiB
2023-06-03T23:55:00.772973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17
Q125
median33
Q341
95-th percentile52
Maximum99
Range85
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.580624
Coefficient of variation (CV)0.3189962
Kurtosis-0.7954771
Mean33.168496
Median Absolute Deviation (MAD)8
Skewness0.17095243
Sum776574
Variance111.94961
MonotonicityNot monotonic
2023-06-03T23:55:00.887123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
33 1286
 
5.5%
28 730
 
3.1%
34 724
 
3.1%
35 719
 
3.1%
25 683
 
2.9%
37 680
 
2.9%
39 678
 
2.9%
27 676
 
2.9%
19 672
 
2.9%
24 672
 
2.9%
Other values (34) 15893
67.9%
ValueCountFrequency (%)
14 301
1.3%
15 373
1.6%
16 348
1.5%
17 366
1.6%
18 568
2.4%
19 672
2.9%
20 625
2.7%
21 629
2.7%
22 639
2.7%
23 623
2.7%
ValueCountFrequency (%)
99 1
 
< 0.1%
56 71
 
0.3%
55 289
1.2%
54 303
1.3%
53 323
1.4%
52 330
1.4%
51 258
1.1%
50 287
1.2%
49 331
1.4%
48 318
1.4%

Annual_Income
Real number (ℝ)

Distinct8316
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167965.67
Minimum7005.93
Maximum23912939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:00.996251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7005.93
5-th percentile9159.02
Q118771.66
median37612.54
Q370956.8
95-th percentile137748.17
Maximum23912939
Range23905933
Interquartile range (IQR)52185.14

Descriptive statistics

Standard deviation1408502.2
Coefficient of variation (CV)8.3856554
Kurtosis174.27379
Mean167965.67
Median Absolute Deviation (MAD)21168.12
Skewness12.939679
Sum3.9325802 × 109
Variance1.9838785 × 1012
MonotonicityNot monotonic
2023-06-03T23:55:01.107936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46855.17 8
 
< 0.1%
33832.58 8
 
< 0.1%
40332.61 8
 
< 0.1%
167753.52 8
 
< 0.1%
54881.55 8
 
< 0.1%
53451.48 7
 
< 0.1%
49675.38 7
 
< 0.1%
145931.88 7
 
< 0.1%
34627.86 7
 
< 0.1%
69932.08 7
 
< 0.1%
Other values (8306) 23338
99.7%
ValueCountFrequency (%)
7005.93 5
< 0.1%
7006.04 4
< 0.1%
7006.52 3
< 0.1%
7011.68 3
< 0.1%
7012.31 3
< 0.1%
7020.54 5
< 0.1%
7021.91 4
< 0.1%
7046.5 5
< 0.1%
7055.84 2
 
< 0.1%
7056.4 3
< 0.1%
ValueCountFrequency (%)
23912939 1
< 0.1%
23871966 1
< 0.1%
23784659 1
< 0.1%
23775314 1
< 0.1%
23498432 1
< 0.1%
23344386 1
< 0.1%
23266988 1
< 0.1%
23070456 1
< 0.1%
22928808 1
< 0.1%
22782091 1
< 0.1%

Monthly_Inhand_Salary
Real number (ℝ)

Distinct8008
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3978.965
Minimum319.56
Maximum15204.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:01.216275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum319.56
5-th percentile843.58
Q11711.76
median3080.56
Q35138.76
95-th percentile10688.28
Maximum15204.63
Range14885.07
Interquartile range (IQR)3427

Descriptive statistics

Standard deviation3017.8009
Coefficient of variation (CV)0.75843866
Kurtosis1.7356396
Mean3978.965
Median Absolute Deviation (MAD)1537.8
Skewness1.4583806
Sum93159507
Variance9107122.1
MonotonicityNot monotonic
2023-06-03T23:55:01.311417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3080.56 3589
 
15.3%
1843.08 10
 
< 0.1%
3491.93 9
 
< 0.1%
1359.58 9
 
< 0.1%
1473.03 9
 
< 0.1%
1462.83 8
 
< 0.1%
4633.46 8
 
< 0.1%
807.6 8
 
< 0.1%
1095.81 8
 
< 0.1%
1092.94 7
 
< 0.1%
Other values (7998) 19748
84.3%
ValueCountFrequency (%)
319.56 3
< 0.1%
332.13 2
 
< 0.1%
332.43 3
< 0.1%
333.6 1
 
< 0.1%
355.21 5
< 0.1%
357.26 5
< 0.1%
358.06 2
 
< 0.1%
368.37 1
 
< 0.1%
378.99 3
< 0.1%
379.39 1
 
< 0.1%
ValueCountFrequency (%)
15204.63 3
< 0.1%
15136.7 4
< 0.1%
15115.19 2
 
< 0.1%
15101.94 1
 
< 0.1%
15091.09 2
 
< 0.1%
15090.08 1
 
< 0.1%
15066.78 3
< 0.1%
15038.32 3
< 0.1%
14978.34 5
< 0.1%
14960.25 4
< 0.1%

Num_Bank_Accounts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct298
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.763038
Minimum0
Maximum1794
Zeros1305
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:01.418016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q38
95-th percentile10
Maximum1794
Range1794
Interquartile range (IQR)5

Descriptive statistics

Standard deviation114.25558
Coefficient of variation (CV)6.8159236
Kurtosis138.54936
Mean16.763038
Median Absolute Deviation (MAD)2
Skewness11.400572
Sum392473
Variance13054.339
MonotonicityNot monotonic
2023-06-03T23:55:01.516252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 2801
12.0%
6 2783
11.9%
8 2751
11.7%
4 2452
10.5%
5 2412
10.3%
3 2372
10.1%
9 1780
7.6%
10 1657
7.1%
2 1410
6.0%
1 1374
5.9%
Other values (288) 1621
6.9%
ValueCountFrequency (%)
0 1305
5.6%
1 1374
5.9%
2 1410
6.0%
3 2372
10.1%
4 2452
10.5%
5 2412
10.3%
6 2783
11.9%
7 2801
12.0%
8 2751
11.7%
9 1780
7.6%
ValueCountFrequency (%)
1794 1
< 0.1%
1793 1
< 0.1%
1786 1
< 0.1%
1779 1
< 0.1%
1778 1
< 0.1%
1766 2
< 0.1%
1756 1
< 0.1%
1748 1
< 0.1%
1733 1
< 0.1%
1719 1
< 0.1%

Num_Credit_Card
Real number (ℝ)

Distinct449
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.776492
Minimum0
Maximum1498
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:01.617244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q37
95-th percentile10
Maximum1498
Range1498
Interquartile range (IQR)3

Descriptive statistics

Standard deviation131.19784
Coefficient of variation (CV)5.7602306
Kurtosis73.830062
Mean22.776492
Median Absolute Deviation (MAD)2
Skewness8.4410055
Sum533266
Variance17212.874
MonotonicityNot monotonic
2023-06-03T23:55:01.712506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 4238
18.1%
7 3630
15.5%
6 3449
14.7%
4 2690
11.5%
3 2508
10.7%
8 1615
 
6.9%
10 1538
 
6.6%
9 1501
 
6.4%
2 857
 
3.7%
1 855
 
3.7%
Other values (439) 532
 
2.3%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 855
 
3.7%
2 857
 
3.7%
3 2508
10.7%
4 2690
11.5%
5 4238
18.1%
6 3449
14.7%
7 3630
15.5%
8 1615
 
6.9%
9 1501
 
6.4%
ValueCountFrequency (%)
1498 2
< 0.1%
1493 1
< 0.1%
1490 2
< 0.1%
1480 1
< 0.1%
1479 1
< 0.1%
1477 2
< 0.1%
1472 1
< 0.1%
1470 1
< 0.1%
1466 1
< 0.1%
1461 2
< 0.1%

Interest_Rate
Real number (ℝ)

Distinct494
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.264298
Minimum1
Maximum5788
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:01.813215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median15
Q324
95-th percentile33
Maximum5788
Range5787
Interquartile range (IQR)17

Descriptive statistics

Standard deviation460.14734
Coefficient of variation (CV)6.3675613
Kurtosis87.387353
Mean72.264298
Median Absolute Deviation (MAD)8
Skewness9.1089655
Sum1691924
Variance211735.58
MonotonicityNot monotonic
2023-06-03T23:55:01.906460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 1154
 
4.9%
5 1026
 
4.4%
11 973
 
4.2%
7 963
 
4.1%
6 919
 
3.9%
12 912
 
3.9%
9 910
 
3.9%
10 853
 
3.6%
4 844
 
3.6%
3 833
 
3.6%
Other values (484) 14026
59.9%
ValueCountFrequency (%)
1 800
3.4%
2 778
3.3%
3 833
3.6%
4 844
3.6%
5 1026
4.4%
6 919
3.9%
7 963
4.1%
8 1154
4.9%
9 910
3.9%
10 853
3.6%
ValueCountFrequency (%)
5788 1
< 0.1%
5774 1
< 0.1%
5773 1
< 0.1%
5752 1
< 0.1%
5751 1
< 0.1%
5745 1
< 0.1%
5733 1
< 0.1%
5732 2
< 0.1%
5729 1
< 0.1%
5721 1
< 0.1%

Num_of_Loan
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct121
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2904796
Minimum0
Maximum1480
Zeros2301
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size205.9 KiB
2023-06-03T23:55:01.997232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile9
Maximum1480
Range1480
Interquartile range (IQR)4

Descriptive statistics

Standard deviation58.67977
Coefficient of variation (CV)8.0488215
Kurtosis382.6649
Mean7.2904796
Median Absolute Deviation (MAD)2
Skewness18.967951
Sum170692
Variance3443.3154
MonotonicityNot monotonic
2023-06-03T23:55:02.088517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 4210
18.0%
2 3409
14.6%
3 3292
14.1%
0 2301
9.8%
6 2114
9.0%
1 2043
8.7%
7 2032
8.7%
5 1911
8.2%
9 1074
 
4.6%
8 914
 
3.9%
Other values (111) 113
 
0.5%
ValueCountFrequency (%)
0 2301
9.8%
1 2043
8.7%
2 3409
14.6%
3 3292
14.1%
4 4210
18.0%
5 1911
8.2%
6 2114
9.0%
7 2032
8.7%
8 914
 
3.9%
9 1074
 
4.6%
ValueCountFrequency (%)
1480 1
< 0.1%
1465 1
< 0.1%
1459 1
< 0.1%
1457 1
< 0.1%
1441 1
< 0.1%
1439 1
< 0.1%
1419 1
< 0.1%
1406 1
< 0.1%
1400 1
< 0.1%
1372 1
< 0.1%

Delay_from_due_date
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.748664
Minimum0
Maximum67
Zeros353
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:02.184718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median19
Q331
95-th percentile56
Maximum67
Range67
Interquartile range (IQR)21

Descriptive statistics

Standard deviation16.381454
Coefficient of variation (CV)0.72010618
Kurtosis-0.34273147
Mean22.748664
Median Absolute Deviation (MAD)10
Skewness0.78434253
Sum532614.48
Variance268.35203
MonotonicityNot monotonic
2023-06-03T23:55:02.570947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 813
 
3.5%
14 758
 
3.2%
10 748
 
3.2%
7 726
 
3.1%
8 722
 
3.1%
13 705
 
3.0%
5 694
 
3.0%
11 682
 
2.9%
6 662
 
2.8%
12 660
 
2.8%
Other values (59) 16243
69.4%
ValueCountFrequency (%)
0 353
1.5%
1 383
1.6%
2 385
1.6%
3 522
2.2%
4 489
2.1%
5 694
3.0%
6 662
2.8%
7 726
3.1%
8 722
3.1%
9 653
2.8%
ValueCountFrequency (%)
67 6
 
< 0.1%
66 11
 
< 0.1%
65 14
 
0.1%
64 24
 
0.1%
63 24
 
0.1%
62 178
0.8%
61 177
0.8%
60 154
0.7%
59 171
0.7%
58 202
0.9%

Num_of_Delayed_Payment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct189
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.028446
Minimum0
Maximum4340
Zeros522
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:02.662833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median14
Q318
95-th percentile24
Maximum4340
Range4340
Interquartile range (IQR)9

Descriptive statistics

Standard deviation214.76019
Coefficient of variation (CV)7.3982668
Kurtosis241.80459
Mean29.028446
Median Absolute Deviation (MAD)5
Skewness15.128667
Sum679643
Variance46121.938
MonotonicityNot monotonic
2023-06-03T23:55:02.743827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 2555
 
10.9%
10 1166
 
5.0%
18 1153
 
4.9%
19 1152
 
4.9%
20 1139
 
4.9%
9 1112
 
4.7%
17 1104
 
4.7%
16 1070
 
4.6%
8 1067
 
4.6%
15 1046
 
4.5%
Other values (179) 10849
46.3%
ValueCountFrequency (%)
0 522
2.2%
1 531
2.3%
2 538
2.3%
3 585
2.5%
4 533
2.3%
5 621
2.7%
6 675
2.9%
7 644
2.8%
8 1067
4.6%
9 1112
4.7%
ValueCountFrequency (%)
4340 1
< 0.1%
4319 1
< 0.1%
4295 1
< 0.1%
4282 1
< 0.1%
4281 1
< 0.1%
4266 1
< 0.1%
4262 1
< 0.1%
4231 1
< 0.1%
4211 1
< 0.1%
4178 1
< 0.1%

Changed_Credit_Limit
Real number (ℝ)

Distinct3217
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6132645
Minimum-6.49
Maximum35.3
Zeros1
Zeros (%)< 0.1%
Negative414
Negative (%)1.8%
Memory size183.0 KiB
2023-06-03T23:55:02.831363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-6.49
5-th percentile1.07
Q14.69
median8.74
Q312.9
95-th percentile23.08
Maximum35.3
Range41.79
Interquartile range (IQR)8.21

Descriptive statistics

Standard deviation6.6376309
Coefficient of variation (CV)0.69046585
Kurtosis0.48330391
Mean9.6132645
Median Absolute Deviation (MAD)4.08
Skewness0.82104312
Sum225075.36
Variance44.058143
MonotonicityNot monotonic
2023-06-03T23:55:02.923825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.613264486 512
 
2.2%
9.25 48
 
0.2%
11.5 45
 
0.2%
7.06 36
 
0.2%
11.49 34
 
0.1%
11.95 34
 
0.1%
10.54 34
 
0.1%
1.63 34
 
0.1%
8.99 33
 
0.1%
3.93 32
 
0.1%
Other values (3207) 22571
96.4%
ValueCountFrequency (%)
-6.49 1
< 0.1%
-6.43 1
< 0.1%
-6.35 1
< 0.1%
-6.33 1
< 0.1%
-6.32 1
< 0.1%
-5.99 1
< 0.1%
-5.93 1
< 0.1%
-5.78 1
< 0.1%
-5.76 1
< 0.1%
-5.74 1
< 0.1%
ValueCountFrequency (%)
35.3 1
< 0.1%
34.91 1
< 0.1%
34.86 1
< 0.1%
34.53 1
< 0.1%
34.48 1
< 0.1%
34.3 1
< 0.1%
34.12 1
< 0.1%
34.01 1
< 0.1%
33.96 1
< 0.1%
33.61 1
< 0.1%

Num_Credit_Inquiries
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct359
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.515056
Minimum0
Maximum2587
Zeros1454
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:03.018053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q310
95-th percentile14
Maximum2587
Range2587
Interquartile range (IQR)7

Descriptive statistics

Standard deviation189.41576
Coefficient of variation (CV)6.884077
Kurtosis102.61186
Mean27.515056
Median Absolute Deviation (MAD)3
Skewness9.890863
Sum644210
Variance35878.331
MonotonicityNot monotonic
2023-06-03T23:55:03.118219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2229
9.5%
4 2163
 
9.2%
3 2033
 
8.7%
2 1751
 
7.5%
8 1731
 
7.4%
7 1728
 
7.4%
1 1718
 
7.3%
11 1590
 
6.8%
9 1541
 
6.6%
0 1454
 
6.2%
Other values (349) 5475
23.4%
ValueCountFrequency (%)
0 1454
6.2%
1 1718
7.3%
2 1751
7.5%
3 2033
8.7%
4 2163
9.2%
5 930
4.0%
6 2229
9.5%
7 1728
7.4%
8 1731
7.4%
9 1541
6.6%
ValueCountFrequency (%)
2587 1
< 0.1%
2572 2
< 0.1%
2564 1
< 0.1%
2551 1
< 0.1%
2547 1
< 0.1%
2544 1
< 0.1%
2542 1
< 0.1%
2540 1
< 0.1%
2529 1
< 0.1%
2521 1
< 0.1%

Credit_Mix
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
3
12561 
1
5854 
2
4998 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Length

2023-06-03T23:55:03.201922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:03.273442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Most occurring characters

ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Outstanding_Debt
Real number (ℝ)

Distinct8018
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1598.1008
Minimum0.23
Maximum4998.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:03.350483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile139.77
Q1695.46
median1361.17
Q32281.65
95-th percentile4126.76
Maximum4998.07
Range4997.84
Interquartile range (IQR)1586.19

Descriptive statistics

Standard deviation1163.5777
Coefficient of variation (CV)0.72810028
Kurtosis0.37930629
Mean1598.1008
Median Absolute Deviation (MAD)763.86
Skewness0.93534478
Sum37416335
Variance1353913
MonotonicityNot monotonic
2023-06-03T23:55:03.449701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2538.81 11
 
< 0.1%
463.57 10
 
< 0.1%
1360.45 10
 
< 0.1%
2696.09 10
 
< 0.1%
3626.94 9
 
< 0.1%
2530.46 9
 
< 0.1%
66.47 9
 
< 0.1%
3572.04 9
 
< 0.1%
796.88 9
 
< 0.1%
1292.14 9
 
< 0.1%
Other values (8008) 23318
99.6%
ValueCountFrequency (%)
0.23 2
< 0.1%
0.54 2
< 0.1%
0.56 1
 
< 0.1%
0.77 2
< 0.1%
1.2 4
< 0.1%
1.3 4
< 0.1%
1.48 3
< 0.1%
2.43 4
< 0.1%
3.68 2
< 0.1%
3.74 1
 
< 0.1%
ValueCountFrequency (%)
4998.07 5
< 0.1%
4997.1 3
< 0.1%
4997.05 1
 
< 0.1%
4992.25 2
 
< 0.1%
4990.91 2
 
< 0.1%
4987.19 2
 
< 0.1%
4986.03 2
 
< 0.1%
4983.86 3
< 0.1%
4975.63 2
 
< 0.1%
4974.81 4
< 0.1%

Credit_Utilization_Ratio
Real number (ℝ)

Distinct23413
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.215666
Minimum20.172942
Maximum49.522324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:03.546770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.172942
5-th percentile24.183841
Q127.976271
median32.250408
Q336.458782
95-th percentile40.152072
Maximum49.522324
Range29.349382
Interquartile range (IQR)8.4825113

Descriptive statistics

Standard deviation5.1215648
Coefficient of variation (CV)0.15897746
Kurtosis-0.94188773
Mean32.215666
Median Absolute Deviation (MAD)4.2351899
Skewness0.032470915
Sum754265.38
Variance26.230426
MonotonicityNot monotonic
2023-06-03T23:55:03.638937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.93466475 1
 
< 0.1%
27.36771187 1
 
< 0.1%
23.55829516 1
 
< 0.1%
37.51398837 1
 
< 0.1%
32.46391157 1
 
< 0.1%
25.35531002 1
 
< 0.1%
26.56402492 1
 
< 0.1%
29.96399943 1
 
< 0.1%
23.95960253 1
 
< 0.1%
37.31756332 1
 
< 0.1%
Other values (23403) 23403
> 99.9%
ValueCountFrequency (%)
20.1729419 1
< 0.1%
20.24413035 1
< 0.1%
20.98560579 1
< 0.1%
20.98591888 1
< 0.1%
20.992914 1
< 0.1%
21.02869026 1
< 0.1%
21.22850297 1
< 0.1%
21.33717658 1
< 0.1%
21.35905054 1
< 0.1%
21.45898741 1
< 0.1%
ValueCountFrequency (%)
49.5223243 1
< 0.1%
48.48985173 1
< 0.1%
47.96956024 1
< 0.1%
47.64242451 1
< 0.1%
47.57875179 1
< 0.1%
47.48366327 1
< 0.1%
47.29400692 1
< 0.1%
47.10340881 1
< 0.1%
46.92057454 1
< 0.1%
46.64453623 1
< 0.1%

Credit_History_Age
Real number (ℝ)

Distinct405
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.49093
Minimum1
Maximum404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:03.738636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile66
Q1143
median213.49093
Q3281
95-th percentile377
Maximum404
Range403
Interquartile range (IQR)138

Descriptive statistics

Standard deviation94.831951
Coefficient of variation (CV)0.44419663
Kurtosis-0.69827891
Mean213.49093
Median Absolute Deviation (MAD)69.509074
Skewness0.053067912
Sum4998463.1
Variance8993.0989
MonotonicityNot monotonic
2023-06-03T23:55:03.836694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213.4909262 2143
 
9.2%
191 115
 
0.5%
231 113
 
0.5%
215 110
 
0.5%
213 109
 
0.5%
212 107
 
0.5%
232 105
 
0.4%
219 105
 
0.4%
233 105
 
0.4%
224 104
 
0.4%
Other values (395) 20297
86.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 6
 
< 0.1%
3 6
 
< 0.1%
4 11
< 0.1%
5 14
0.1%
6 11
< 0.1%
7 11
< 0.1%
8 21
0.1%
9 20
0.1%
10 20
0.1%
ValueCountFrequency (%)
404 2
 
< 0.1%
403 1
 
< 0.1%
402 10
 
< 0.1%
401 13
 
0.1%
400 21
 
0.1%
399 34
0.1%
398 41
0.2%
397 43
0.2%
396 52
0.2%
395 59
0.3%

Total_EMI_per_month
Real number (ℝ)

Distinct8198
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1342.8789
Minimum0
Maximum82236
Zeros2162
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:03.938016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133.767386
median73.981294
Q3169.936
95-th percentile472.36108
Maximum82236
Range82236
Interquartile range (IQR)136.16862

Descriptive statistics

Standard deviation8027.0441
Coefficient of variation (CV)5.9774892
Kurtosis55.867748
Mean1342.8789
Median Absolute Deviation (MAD)52.45971
Skewness7.3245001
Sum31440824
Variance64433437
MonotonicityNot monotonic
2023-06-03T23:55:04.028690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2162
 
9.2%
218.6546697 8
 
< 0.1%
145.5524217 8
 
< 0.1%
150.2708913 8
 
< 0.1%
40.92697968 7
 
< 0.1%
77.91293447 7
 
< 0.1%
110.1315508 7
 
< 0.1%
35.38951471 7
 
< 0.1%
19.80976191 7
 
< 0.1%
146.7012526 7
 
< 0.1%
Other values (8188) 21185
90.5%
ValueCountFrequency (%)
0 2162
9.2%
4.713183572 5
 
< 0.1%
5.24927327 4
 
< 0.1%
5.262291048 3
 
< 0.1%
5.629824417 1
 
< 0.1%
5.905518076 2
 
< 0.1%
5.994895587 1
 
< 0.1%
6.047450347 3
 
< 0.1%
6.412118995 4
 
< 0.1%
6.442169892 2
 
< 0.1%
ValueCountFrequency (%)
82236 1
< 0.1%
82095 1
< 0.1%
81971 1
< 0.1%
81209 1
< 0.1%
80981 1
< 0.1%
80768 1
< 0.1%
80679 1
< 0.1%
80602 1
< 0.1%
80501 1
< 0.1%
80497 1
< 0.1%

Amount_invested_monthly
Real number (ℝ)

Distinct21223
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean623.40594
Minimum0
Maximum10000
Zeros56
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:04.122950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.160878
Q176.235889
median134.35556
Q3252.70465
95-th percentile1145.1946
Maximum10000
Range10000
Interquartile range (IQR)176.46876

Descriptive statistics

Standard deviation2018.9814
Coefficient of variation (CV)3.2386304
Kurtosis17.435479
Mean623.40594
Median Absolute Deviation (MAD)70.798083
Skewness4.3860455
Sum14595803
Variance4076285.9
MonotonicityNot monotonic
2023-06-03T23:55:04.220498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134.35556 1109
 
4.7%
10000 1028
 
4.4%
0 56
 
0.2%
208.8959183 1
 
< 0.1%
232.1775582 1
 
< 0.1%
48.00806666 1
 
< 0.1%
90.84269353 1
 
< 0.1%
136.0445319 1
 
< 0.1%
204.6386045 1
 
< 0.1%
145.5815241 1
 
< 0.1%
Other values (21213) 21213
90.6%
ValueCountFrequency (%)
0 56
0.2%
10.11661404 1
 
< 0.1%
10.1225566 1
 
< 0.1%
10.14128456 1
 
< 0.1%
10.14343562 1
 
< 0.1%
10.20080048 1
 
< 0.1%
10.2494613 1
 
< 0.1%
10.28340438 1
 
< 0.1%
10.33620331 1
 
< 0.1%
10.35788829 1
 
< 0.1%
ValueCountFrequency (%)
10000 1028
4.4%
1961.21885 1
 
< 0.1%
1941.237454 1
 
< 0.1%
1885.645318 1
 
< 0.1%
1860.919693 1
 
< 0.1%
1804.332527 1
 
< 0.1%
1804.233521 1
 
< 0.1%
1785.786788 1
 
< 0.1%
1729.241436 1
 
< 0.1%
1684.861491 1
 
< 0.1%

Monthly_Balance
Real number (ℝ)

Distinct23125
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395.05717
Minimum0.095482496
Maximum1602.0405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-03T23:55:04.318441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.095482496
5-th percentile171.36689
Q1266.9128
median330.56261
Q3452.53029
95-th percentile867.08216
Maximum1602.0405
Range1601.945
Interquartile range (IQR)185.61749

Descriptive statistics

Standard deviation214.43463
Coefficient of variation (CV)0.54279392
Kurtosis3.5236459
Mean395.05717
Median Absolute Deviation (MAD)79.41909
Skewness1.7385404
Sum9249473.4
Variance45982.209
MonotonicityNot monotonic
2023-06-03T23:55:04.413263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
395.0571653 289
 
1.2%
246.4845984 1
 
< 0.1%
814.432899 1
 
< 0.1%
833.4292499 1
 
< 0.1%
288.3921991 1
 
< 0.1%
299.542164 1
 
< 0.1%
296.5068107 1
 
< 0.1%
373.2708014 1
 
< 0.1%
240.0178673 1
 
< 0.1%
239.245076 1
 
< 0.1%
Other values (23115) 23115
98.7%
ValueCountFrequency (%)
0.09548249602 1
< 0.1%
0.3661470795 1
< 0.1%
0.4191236108 1
< 0.1%
0.4534564914 1
< 0.1%
0.5035823529 1
< 0.1%
0.599640126 1
< 0.1%
0.688298779 1
< 0.1%
0.7102397102 1
< 0.1%
0.9081458437 1
< 0.1%
1.526310326 1
< 0.1%
ValueCountFrequency (%)
1602.040519 1
< 0.1%
1566.613165 1
< 0.1%
1558.421841 1
< 0.1%
1555.201051 1
< 0.1%
1528.744936 1
< 0.1%
1507.553363 1
< 0.1%
1478.421322 1
< 0.1%
1474.356118 1
< 0.1%
1468.313963 1
< 0.1%
1463.792328 1
< 0.1%

Credit_Score
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
14499 
1
8914 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%

Length

2023-06-03T23:55:04.502072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:04.567261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%

Most occurring characters

ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21920 
1
 
1493

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%

Length

2023-06-03T23:55:04.623368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:04.689142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21945 
1
 
1468

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%

Length

2023-06-03T23:55:04.745380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:04.810026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21989 
1
 
1424

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%

Length

2023-06-03T23:55:04.865891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:04.933489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
22024 
1
 
1389

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%

Length

2023-06-03T23:55:04.991832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:05.058147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21853 
1
 
1560

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%

Length

2023-06-03T23:55:05.118405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:05.190520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21937 
1
 
1476

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%

Length

2023-06-03T23:55:05.259531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:05.331216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21982 
1
 
1431

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%

Length

2023-06-03T23:55:05.389259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:05.457519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21943 
1
 
1470

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%

Length

2023-06-03T23:55:05.516421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:05.582579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21965 
1
 
1448

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%

Length

2023-06-03T23:55:05.634595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:05.695946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21967 
1
 
1446

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%

Length

2023-06-03T23:55:05.750788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:05.815327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
22059 
1
 
1354

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%

Length

2023-06-03T23:55:05.870066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:05.934807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
22017 
1
 
1396

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%

Length

2023-06-03T23:55:05.989537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:06.055741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20194 
1
3219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%

Length

2023-06-03T23:55:06.113250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:06.182667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%

Most occurring characters

ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21867 
1
 
1546

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%

Length

2023-06-03T23:55:06.243170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:06.311917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
22120 
1
 
1293

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%

Length

2023-06-03T23:55:06.369101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:06.434396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20507 
1
2906 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%

Length

2023-06-03T23:55:06.490990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:06.557664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
14273 
1
9140 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%

Length

2023-06-03T23:55:06.615939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:06.682062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%

Most occurring characters

ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
12046 
1
11367 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%

Length

2023-06-03T23:55:06.738407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:06.804106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%

Most occurring characters

ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20303 
1
3110 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%

Length

2023-06-03T23:55:06.862656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:06.927956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
19528 
1
3885 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%

Length

2023-06-03T23:55:06.984957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:07.049769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20782 
1
2631 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%

Length

2023-06-03T23:55:07.108261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:07.173299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20985 
1
2428 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%

Length

2023-06-03T23:55:07.230360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:07.294917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%

Most occurring characters

ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20033 
1
3380 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%

Length

2023-06-03T23:55:07.353475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:07.419454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%

Most occurring characters

ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
15434 
1
7979 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Length

2023-06-03T23:55:07.477270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-03T23:55:07.543090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Most occurring characters

ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Interactions

2023-06-03T23:54:57.749370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:26.440202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:27.694721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:29.658447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:31.403897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:33.263414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:35.128666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:38.707250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:40.880225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:42.993863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:44.670000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:46.796498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:48.777252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:50.694646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:52.453928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:54.445713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:56.107700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:57.846020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:26.512901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:27.800561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:29.752265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:31.493495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:33.356118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:35.249178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:38.893101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:40.974825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:43.093266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:44.758939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:46.902354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:48.871126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:50.785317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:52.551303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:54.527321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:56.202011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:57.946118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:26.586384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:27.914961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:29.856514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:31.590488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:33.451352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:35.363354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:39.032048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:41.094286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:43.189945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:44.878849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:47.009217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:48.974403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:50.883502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:52.652701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:54.632254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:56.299238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:58.053128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:26.660433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:28.023124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:29.959936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:31.690746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:33.549798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:35.470301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:39.134976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:41.227968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:43.295286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:44.981220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:47.207281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:49.081513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:50.985784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:52.756958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:54.737113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:56.402127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:58.155832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:26.732578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:28.117798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:30.066284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:31.790203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:33.650422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:35.577866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:39.236401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:41.445278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:43.394054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:45.084157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:47.350663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-06-03T23:54:40.776555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:42.874956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:44.569887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:46.686225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:48.660658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:50.590584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:52.281764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:54.355622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:55.995455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-03T23:54:57.645624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-06-03T23:55:07.646849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
AgeAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesOutstanding_DebtCredit_Utilization_RatioCredit_History_AgeTotal_EMI_per_monthAmount_invested_monthlyMonthly_BalanceCredit_MixCredit_ScoreOccupation_AccountantOccupation_ArchitectOccupation_DeveloperOccupation_DoctorOccupation_EngineerOccupation_EntrepreneurOccupation_JournalistOccupation_LawyerOccupation_ManagerOccupation_MechanicOccupation_Media_ManagerOccupation_MusicianOccupation_ScientistOccupation_TeacherOccupation_WriterPayment_of_Min_Amount_NMPayment_of_Min_Amount_NoPayment_of_Min_Amount_YesPayment_Behaviour_High_spent_Large_value_paymentsPayment_Behaviour_High_spent_Medium_value_paymentsPayment_Behaviour_High_spent_Small_value_paymentsPayment_Behaviour_Low_spent_Large_value_paymentsPayment_Behaviour_Low_spent_Medium_value_paymentsPayment_Behaviour_Low_spent_Small_value_payments
Age1.0000.0960.079-0.205-0.161-0.230-0.201-0.197-0.197-0.131-0.228-0.2390.0180.217-0.0750.0560.1300.1830.2530.0000.0230.0070.0240.0220.0260.0330.0180.0270.0070.0000.0260.0000.0310.0220.0000.3050.2960.0530.0000.0000.0000.0120.049
Annual_Income0.0961.0000.891-0.312-0.251-0.329-0.277-0.285-0.295-0.161-0.318-0.3390.1440.3040.4730.5640.5770.0090.0110.0080.0000.0110.0000.0070.0000.0000.0390.0000.0000.0000.0070.0150.0000.0000.0000.0170.0000.0090.0000.0080.0000.0160.000
Monthly_Inhand_Salary0.0790.8911.000-0.264-0.214-0.284-0.231-0.242-0.251-0.134-0.268-0.2890.1230.2600.4430.5220.5340.2260.3120.0260.0170.0240.0240.0290.0200.0220.0280.0390.0130.0240.0300.0150.0210.0280.0000.3560.3490.2240.1790.0620.0380.1110.267
Num_Bank_Accounts-0.205-0.312-0.2641.0000.4990.6180.5240.6350.6260.2640.5900.613-0.085-0.5580.129-0.189-0.3570.0000.0000.0000.0200.0000.0000.0000.0150.0140.0000.0070.0090.0120.0000.0060.0110.0000.0000.0170.0060.0000.0000.0030.0130.0210.000
Num_Credit_Card-0.161-0.251-0.2140.4991.0000.5010.4220.5150.4680.2060.4840.518-0.063-0.4530.099-0.149-0.2900.0000.0000.0130.0000.0000.0120.0080.0080.0100.0000.0070.0000.0030.0080.0210.0000.0100.0270.0180.0120.0000.0000.0000.0230.0000.000
Interest_Rate-0.230-0.329-0.2840.6180.5011.0000.5630.6010.5810.3190.6570.681-0.082-0.6170.148-0.199-0.3810.0000.0210.0000.0000.0090.0070.0000.0000.0120.0000.0090.0040.0090.0120.0110.0000.0150.0000.0150.0110.0000.0180.0000.0090.0180.009
Num_of_Loan-0.201-0.277-0.2310.5240.4220.5631.0000.5120.5150.2600.5500.632-0.102-0.5730.468-0.168-0.4870.0030.0050.0030.0000.0170.0080.0000.0000.0110.0000.0000.0000.0000.0000.0130.0000.0000.0000.0130.0140.0000.0000.0000.0210.0040.000
Delay_from_due_date-0.197-0.285-0.2420.6350.5150.6010.5121.0000.5960.2390.5780.613-0.072-0.5500.145-0.175-0.3420.4560.5840.0180.0420.0150.0280.0190.0100.0370.0130.0230.0170.0090.0210.0240.0350.0310.0000.5890.5780.0860.0390.0000.0000.0070.096
Num_of_Delayed_Payment-0.197-0.295-0.2510.6260.4680.5810.5150.5961.0000.2220.5480.588-0.087-0.5360.141-0.177-0.3510.0000.0010.0000.0000.0040.0000.0000.0000.0000.0140.0190.0110.0110.0000.0000.0080.0130.0000.0000.0000.0230.0040.0220.0110.0000.000
Changed_Credit_Limit-0.131-0.161-0.1340.2640.2060.3190.2600.2390.2221.0000.3130.352-0.041-0.3400.074-0.094-0.1740.3570.2880.0310.0150.0220.0080.0130.0270.0270.0070.0290.0370.0230.0000.0000.0020.0260.0000.4150.4050.0540.0070.0160.0000.0000.048
Num_Credit_Inquiries-0.228-0.318-0.2680.5900.4840.6570.5500.5780.5480.3131.0000.658-0.084-0.6130.153-0.182-0.3730.0100.0000.0000.0000.0000.0110.0000.0000.0050.0000.0000.0070.0280.0050.0110.0000.0050.0060.0000.0150.0100.0000.0000.0110.0000.000
Outstanding_Debt-0.239-0.339-0.2890.6130.5180.6810.6320.6130.5880.3520.6581.000-0.088-0.6750.165-0.200-0.4110.5130.6480.0100.0270.0360.0190.0410.0300.0240.0230.0500.0390.0330.0350.0000.0340.0200.0000.7030.6900.1120.0420.0000.0000.0000.121
Credit_Utilization_Ratio0.0180.1440.123-0.085-0.063-0.082-0.102-0.072-0.087-0.041-0.084-0.0881.0000.0680.0120.0310.1900.0820.0970.0000.0000.0140.0000.0080.0130.0050.0030.0090.0000.0150.0000.0000.0000.0110.0040.1250.1200.1520.0560.0160.0290.0420.092
Credit_History_Age0.2170.3040.260-0.558-0.453-0.617-0.573-0.550-0.536-0.340-0.613-0.6750.0681.000-0.1560.1830.3730.4270.5620.0150.0230.0270.0220.0000.0430.0250.0250.0260.0340.0150.0120.0280.0260.0290.0140.6500.6390.0950.0290.0190.0070.0000.105
Total_EMI_per_month-0.0750.4730.4430.1290.0990.1480.4680.1450.1410.0740.1530.1650.012-0.1561.0000.2690.0300.0000.0130.0000.0000.0140.0120.0000.0000.0000.0000.0000.0000.0050.0140.0000.0100.0000.0070.0000.0000.0090.0180.0000.0080.0030.000
Amount_invested_monthly0.0560.5640.522-0.189-0.149-0.199-0.168-0.175-0.177-0.094-0.182-0.2000.0310.1830.2691.000-0.0050.0520.1050.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0100.0050.0110.0000.0000.0080.0860.0840.0400.0460.0350.0000.0600.044
Monthly_Balance0.1300.5770.534-0.357-0.290-0.381-0.487-0.342-0.351-0.174-0.373-0.4110.1900.3730.030-0.0051.0000.2590.3250.0070.0030.0110.0070.0160.0000.0230.0190.0190.0120.0040.0250.0000.0000.0110.0000.3940.3850.4090.2670.0830.0890.1050.349
Credit_Mix0.1830.0090.2260.0000.0000.0000.0030.4560.0000.3570.0100.5130.0820.4270.0000.0520.2591.0000.5330.0000.0000.0000.0110.0060.0000.0100.0000.0220.0200.0260.0130.0000.0000.0260.0000.6630.6510.0900.0310.0090.0000.0040.095
Credit_Score0.2530.0110.3120.0000.0000.0210.0050.5840.0010.2880.0000.6480.0970.5620.0130.1050.3250.5331.0000.0040.0000.0040.0000.0000.0050.0080.0000.0000.0110.0190.0160.0000.0000.0240.0030.5940.5850.1100.0540.0230.0000.0030.135
Occupation_Accountant0.0000.0080.0260.0000.0130.0000.0030.0180.0000.0310.0000.0100.0000.0150.0000.0000.0070.0000.0041.0000.0670.0660.0650.0690.0670.0660.0670.0660.0660.0640.0650.1040.0690.0620.0130.0000.0000.0000.0000.0040.0000.0000.000
Occupation_Architect0.0230.0000.0170.0200.0000.0000.0000.0420.0000.0150.0000.0270.0000.0230.0000.0000.0030.0000.0000.0671.0000.0650.0640.0680.0660.0650.0660.0660.0660.0630.0640.1030.0680.0620.0000.0000.0000.0000.0000.0000.0000.0000.000
Occupation_Developer0.0070.0110.0240.0000.0000.0090.0170.0150.0040.0220.0000.0360.0140.0270.0140.0000.0110.0000.0040.0660.0651.0000.0630.0670.0650.0640.0650.0650.0650.0620.0630.1010.0670.0610.0000.0100.0030.0000.0040.0040.0000.0030.000
Occupation_Doctor0.0240.0000.0240.0000.0120.0070.0080.0280.0000.0080.0110.0190.0000.0220.0120.0000.0070.0110.0000.0650.0640.0631.0000.0660.0640.0630.0640.0640.0640.0610.0630.1000.0660.0600.0000.0080.0100.0000.0080.0000.0050.0120.000
Occupation_Engineer0.0220.0070.0290.0000.0080.0000.0000.0190.0000.0130.0000.0410.0080.0000.0000.0050.0160.0060.0000.0690.0680.0670.0661.0000.0690.0670.0680.0680.0680.0660.0670.1060.0700.0640.0000.0000.0000.0070.0000.0040.0040.0000.000
Occupation_Entrepreneur0.0260.0000.0200.0150.0080.0000.0000.0100.0000.0270.0000.0300.0130.0430.0000.0000.0000.0000.0050.0670.0660.0650.0640.0691.0000.0650.0660.0660.0660.0640.0650.1030.0680.0620.0010.0000.0000.0040.0000.0000.0000.0000.000
Occupation_Journalist0.0330.0000.0220.0140.0100.0120.0110.0370.0000.0270.0050.0240.0050.0250.0000.0000.0230.0100.0080.0660.0650.0640.0630.0670.0651.0000.0650.0650.0650.0620.0640.1010.0670.0610.0030.0000.0000.0000.0000.0070.0000.0000.000
Occupation_Lawyer0.0180.0390.0280.0000.0000.0000.0000.0130.0140.0070.0000.0230.0030.0250.0000.0000.0190.0000.0000.0670.0660.0650.0640.0680.0660.0651.0000.0660.0660.0630.0640.1030.0680.0620.0060.0000.0000.0060.0000.0000.0000.0000.000
Occupation_Manager0.0270.0000.0390.0070.0070.0090.0000.0230.0190.0290.0000.0500.0090.0260.0000.0000.0190.0220.0000.0660.0660.0650.0640.0680.0660.0650.0661.0000.0650.0630.0640.1020.0680.0610.0050.0050.0120.0000.0100.0050.0000.0030.000
Occupation_Mechanic0.0070.0000.0130.0090.0000.0040.0000.0170.0110.0370.0070.0390.0000.0340.0000.0000.0120.0200.0110.0660.0660.0650.0640.0680.0660.0650.0660.0651.0000.0630.0640.1020.0680.0610.0000.0000.0000.0010.0000.0000.0070.0000.000
Occupation_Media_Manager0.0000.0000.0240.0120.0030.0090.0000.0090.0110.0230.0280.0330.0150.0150.0050.0100.0040.0260.0190.0640.0630.0620.0610.0660.0640.0620.0630.0630.0631.0000.0620.0980.0650.0590.0110.0060.0160.0000.0000.0090.0000.0020.000
Occupation_Musician0.0260.0070.0300.0000.0080.0120.0000.0210.0000.0000.0050.0350.0000.0120.0140.0050.0250.0130.0160.0650.0640.0630.0630.0670.0650.0640.0640.0640.0640.0621.0000.1000.0660.0600.0000.0100.0070.0080.0000.0070.0000.0000.000
Occupation_Scientist0.0000.0150.0150.0060.0210.0110.0130.0240.0000.0000.0110.0000.0000.0280.0000.0110.0000.0000.0000.1040.1030.1010.1000.1060.1030.1010.1030.1020.1020.0980.1001.0000.1060.0960.0000.0000.0000.0000.0000.0080.0000.0040.000
Occupation_Teacher0.0310.0000.0210.0110.0000.0000.0000.0350.0080.0020.0000.0340.0000.0260.0100.0000.0000.0000.0000.0690.0680.0670.0660.0700.0680.0670.0680.0680.0680.0650.0660.1061.0000.0640.0000.0070.0080.0000.0000.0060.0000.0000.000
Occupation_Writer0.0220.0000.0280.0000.0100.0150.0000.0310.0130.0260.0050.0200.0110.0290.0000.0000.0110.0260.0240.0620.0620.0610.0600.0640.0620.0610.0620.0610.0610.0590.0600.0960.0641.0000.0000.0100.0090.0000.0000.0070.0000.0040.000
Payment_of_Min_Amount_NM0.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0060.0000.0040.0140.0070.0080.0000.0000.0030.0130.0000.0000.0000.0000.0010.0030.0060.0050.0000.0110.0000.0000.0000.0001.0000.3010.3650.0000.0000.0060.0000.0040.000
Payment_of_Min_Amount_No0.3050.0170.3560.0170.0180.0150.0130.5890.0000.4150.0000.7030.1250.6500.0000.0860.3940.6630.5940.0000.0000.0100.0080.0000.0000.0000.0000.0050.0000.0060.0100.0000.0070.0100.3011.0000.7770.1020.0310.0130.0000.0000.111
Payment_of_Min_Amount_Yes0.2960.0000.3490.0060.0120.0110.0140.5780.0000.4050.0150.6900.1200.6390.0000.0840.3850.6510.5850.0000.0000.0030.0100.0000.0000.0000.0000.0120.0000.0160.0070.0000.0080.0090.3650.7771.0000.1010.0320.0050.0000.0010.108
Payment_Behaviour_High_spent_Large_value_payments0.0530.0090.2240.0000.0000.0000.0000.0860.0230.0540.0100.1120.1520.0950.0090.0400.4090.0900.1100.0000.0000.0000.0000.0070.0040.0000.0060.0000.0010.0000.0080.0000.0000.0000.0000.1020.1011.0000.1740.1390.1330.1600.281
Payment_Behaviour_High_spent_Medium_value_payments0.0000.0000.1790.0000.0000.0180.0000.0390.0040.0070.0000.0420.0560.0290.0180.0460.2670.0310.0540.0000.0000.0040.0080.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0310.0320.1741.0000.1580.1510.1830.321
Payment_Behaviour_High_spent_Small_value_payments0.0000.0080.0620.0030.0000.0000.0000.0000.0220.0160.0000.0000.0160.0190.0000.0350.0830.0090.0230.0040.0000.0040.0000.0040.0000.0070.0000.0050.0000.0090.0070.0080.0060.0070.0060.0130.0050.1390.1581.0000.1210.1460.256
Payment_Behaviour_Low_spent_Large_value_payments0.0000.0000.0380.0130.0230.0090.0210.0000.0110.0000.0110.0000.0290.0070.0080.0000.0890.0000.0000.0000.0000.0000.0050.0040.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.1330.1510.1211.0000.1390.244
Payment_Behaviour_Low_spent_Medium_value_payments0.0120.0160.1110.0210.0000.0180.0040.0070.0000.0000.0000.0000.0420.0000.0030.0600.1050.0040.0030.0000.0000.0030.0120.0000.0000.0000.0000.0030.0000.0020.0000.0040.0000.0040.0040.0000.0010.1600.1830.1460.1391.0000.295
Payment_Behaviour_Low_spent_Small_value_payments0.0490.0000.2670.0000.0000.0090.0000.0960.0000.0480.0000.1210.0920.1050.0000.0440.3490.0950.1350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1110.1080.2810.3210.2560.2440.2951.000

Missing values

2023-06-03T23:54:59.556745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-03T23:55:00.371820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgeTotal_EMI_per_monthAmount_invested_monthlyMonthly_BalanceCredit_ScoreOccupation_AccountantOccupation_ArchitectOccupation_DeveloperOccupation_DoctorOccupation_EngineerOccupation_EntrepreneurOccupation_JournalistOccupation_LawyerOccupation_ManagerOccupation_MechanicOccupation_Media_ManagerOccupation_MusicianOccupation_ScientistOccupation_TeacherOccupation_WriterPayment_of_Min_Amount_NMPayment_of_Min_Amount_NoPayment_of_Min_Amount_YesPayment_Behaviour_High_spent_Large_value_paymentsPayment_Behaviour_High_spent_Medium_value_paymentsPayment_Behaviour_High_spent_Small_value_paymentsPayment_Behaviour_Low_spent_Large_value_paymentsPayment_Behaviour_Low_spent_Medium_value_paymentsPayment_Behaviour_Low_spent_Small_value_payments
01831633.542930.131.05836.06.012.704.03846.4537.934665266.050.768440275.759795246.4845980000000001000000010000010
13133446.443080.566.0624621.019.022.4513.032953.6837.895848115.0133.35590593.650442348.8139870000000000100000001000100
23721212.421910.701.041246.014.03.084.03479.8336.491037277.050.305036172.477693238.2874381000000000001000010000100
33360938.135163.1810.0831824.018.012.499.013947.2421.74488458.0378.304673166.487676231.5254010000100000000000001001000
41873057.165998.104.0632224.014.010.0014.022569.0927.350833114.091.35418866.232154692.2233250000000100000000001010000
52540782.693157.562.067219.014.07.683.031233.2438.323940343.039.548996155.936875410.2698790001000000000000010000001
62962848.884546.217.078516.019.015.344.022680.8738.70030581.0767.947345206.017263317.2679041100000000000000001001000
74220204.273080.564.0519418.09.016.509.02335.0432.860511241.027.57867277.279445369.9108000000000000000100100000001
83494312.057702.345.065113.011.00.533.03118.8037.561138395.061.367912616.888909361.9769290000000000010000100000100
92318283.911747.666.01029627.020.03.8710.012521.4029.262379116.054.72120174.865187335.1795290000000010000000001000001
AgeAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgeTotal_EMI_per_monthAmount_invested_monthlyMonthly_BalanceCredit_ScoreOccupation_AccountantOccupation_ArchitectOccupation_DeveloperOccupation_DoctorOccupation_EngineerOccupation_EntrepreneurOccupation_JournalistOccupation_LawyerOccupation_ManagerOccupation_MechanicOccupation_Media_ManagerOccupation_MusicianOccupation_ScientistOccupation_TeacherOccupation_WriterPayment_of_Min_Amount_NMPayment_of_Min_Amount_NoPayment_of_Min_Amount_YesPayment_Behaviour_High_spent_Large_value_paymentsPayment_Behaviour_High_spent_Medium_value_paymentsPayment_Behaviour_High_spent_Small_value_paymentsPayment_Behaviour_Low_spent_Large_value_paymentsPayment_Behaviour_Low_spent_Medium_value_paymentsPayment_Behaviour_Low_spent_Small_value_payments
234034731895.072488.923.062412.010.03.062.03220.3331.023495213.49092660.32284529.857576408.7118280010000000000000100010000
234042215088.351231.367.0829338.019.08.886.012126.6726.396729192.00000037.104879102.261844273.7695270000000000000010001000001
234052719653.611800.809.0525725.020.025.6711.013838.0033.404339165.00000063.36866349.648561327.0628590000001000000000001001000
23406418283.98764.338.0417526.09.014.476.021225.0629.029478148.00000027.71767077.687855261.0276830000000000100000001000001
234071419556.161865.687.0733534.015.021.559.032850.9622.700326213.49092665.797370134.355560258.8707590000000001000000001000001
234083857810.684896.567.057429.016.07.378.031419.9927.141606364.000000131.871482416.933866230.8503181000000000010000010000001
234093715597.311456.788.059515.019.011.306.02741.4632.41800066.00000045.804440142.100773237.7723701000000000001000001000010
2341033179948.8414836.740.054330.00.0-4.654.031104.3131.508604380.000000446.25934710000.000000309.4973620000000000000010010000100
23411327821.24468.778.0111229662.017.019.347.012924.7633.575214123.00000019.72792320.269324286.8797520000100000000000001000010
234124120889.651710.806.01019554.020.028.0512.034323.5530.04584660.00000043.676463197.502352219.9016010000000010000000001000001